Data Engineering Bootcamp

Prepare For A Data Engineering Career

What You Will Learn

  • Python Fundamentals

    Data Engineering with Python Foundations Bootcamp Only

    Master core Python programming required to write clean, executable code from scratch. You will learn syntax, data types, collections, control flow, modular functions, exceptions, and file handling across common formats (CSV, JSON, Parquet). You will gain hands-on experience setting up isolated virtual environments, installing dependencies with pip, requesting data from APIs using httpx, and using standard debugging tools directly within VS Code and the CLI.
  • Python Development & Testing

    Data Engineering with Python Foundations Bootcamp Only

    Transition from basic scripting to structured development workflows. You will learn to safely manage code changes using Git and GitHub (branching, commits, diffs, and merge flows) through VS Code. You will implement basic testing with pytest, track execution using logging (Loglyte), and structure state using Python classes. Through source-to-output reconciliation and data validation checks, you will learn to build repeatable scripts and answer the core engineering question: How do I know the data I made is the data I was supposed to make?
  • Relational Databases & SQL Foundations

    Data Engineering with Python Foundations Bootcamp Only

    Build a solid foundation in database design, data modeling, and relational querying using PostgreSQL and Docker. You will write SQL queries to filter, aggregate, group, and join data across tables. You will learn to construct relational schemas using CREATE TABLE and Data Manipulation Language (DML), enforce primary/foreign keys and referential integrity, and programmatically integrate Python with PostgreSQL to validate and load structured datasets.
  • The Data Engineering Lifecycle

    Learn how raw data becomes a reliable asset across its entire lifecycle: ingestion, storage, transformation, orchestration, and delivery. You will gain hands-on experience making architectural design choices, evaluating compute and storage trade-offs, and embedding security, data quality validation, and observability into every step of the pipeline.
  • Ingesting Data & Data Contracts

    Pull structured and unstructured data from REST APIs, relational databases, flat files, and cloud object storage. You will adopt production engineering standards using modern project setup tooling (uv), dataclasses, and boundary validation with Pydantic. You will build resilient ingestion pipelines with pagination, authentication, retries, and idempotency while using explicit contracts to isolate malformed data into quarantine workflows.
  • Data Storage & Lakehouse Architecture

    Navigate storage selections based on analytical workload, access patterns, and scale. You will work across relational databases (PostgreSQL), embedded analytical engines (DuckDB), cloud object storage (AWS S3), and modern Data Lakehouse architectures using columnar formats (Parquet) and catalogs. You will evaluate partitioning strategies and learn when to leverage distributed processing engines like Apache Spark and Databricks.
  • Data Transformation & Analytics Engineering

    Convert raw source data into clean, business-ready analytical models. You will write advanced SQL using CTEs, window functions, and query execution plan analysis to optimize query performance. You will master analytics engineering with dbt, building modular transformation models, tracking automated data lineage, generating documentation, and enforcing data quality contracts with automated tests.
  • Data Delivery & Event-Driven Systems

    Deliver reliable datasets to downstream analytics platforms, machine learning models, and production endpoints. In addition to scheduled batch delivery, you will construct event-driven architectures using cloud object storage event listeners, message queues, topics, and consumer services. You will address streaming patterns, handle out-of-order event delivery, and manage low-latency data routing.
  • Orchestration & Pipeline Resiliency

    Transform isolated scripts into production-ready, automated workflows using Apache Airflow. You will design Directed Acyclic Graphs (DAGs), configure dependencies, schedule backfills, and implement failure handling. You will master observability, task state management, SLAs, and defensive checkpointing to ensure batch and event pipelines execute reliably without duplicate side effects.
  • AI Engineering & Pipeline Integration

    Treat Generative AI models as powerful but non-deterministic pipeline dependencies. Rather than basic prompt engineering, you will integrate local and hosted Large Language Models (LLMs) into data pipelines for structured extraction, document classification, and entity resolution. You will enforce strict output validation with Pydantic, optimize API costs via response/prompt caching and batching, track token telemetry, and design deterministic fallback mechanisms.
  • Tools and Techniques

    Work with the modern data engineering toolchain and software development practices. You will gain experience with VS Code, Git/GitHub, Docker, uv, pytest, dbt, Apache Airflow, DuckDB, PostgreSQL, Apache Spark/Databricks, and AWS S3. You will operate in agile team settings, conduct code reviews, document architectures, and communicate technical design trade-offs effectively.
  • Real-World Projects & Capstone

    Apply your learning across team-based engineering labs and an individual capstone project. Working with real-world datasets across healthcare, automotive, and public data domains, you will design, deploy, and defend an end-to-end data platform that demonstrates data quality validation, automated testing, observability, and cost-aware architecture.
  • Career Preparation & Post-Graduation Support

    Prepare to launch your career through direct career coaching and industry networking. You will interact with practicing data engineers, participate in technical resume and portfolio workshops, practice system design and coding interviews, and present your work to prospective employers at Demo Day. Post-graduation, you maintain access to dedicated job search support, community sessions, and continuing education seats.

Data Engineering with Python Foundations Bootcamp


Data Engineering Bootcamp


Upcoming Sessions

Program Type Dates Tuition
Data Engineering with Python Fundamentals Bootcamp
Evening (12 Months) January 12, 2027 -
January 14, 2028 		$14,000 Apply Now
Data Engineering Bootcamp
Evening (9 Months) March 23, 2027 -
January 14, 2028 		$10,500 Apply Now



Requirements

Student requirements

  • A growth mindset, anyone can learn to program as long as failure doesn’t frustrate them, failure and not understanding is part of the process.

  • At least 18 years of age

  • American citizen or legally able to work in the U.S.

  • Due to regulatory constraints, we are unable to accept students residing in the state of California at this time.

Student requirements the Data Engineering Bootcamp

  • The Data Engineering Bootcamp is designed for learners who already know how to write code and query data and are ready to move from individual scripts and database exercises into production-oriented data engineering systems.

  • Prior experience with Python, SQL, Git, and relational databases and should be comfortable building small programs independently.

  • Write Python using functions, loops, conditionals, lists, dictionaries, sets, and basic file I/O.

  • Read and write CSV and JSON data and perform basic data cleaning or transformation.

  • Write SQL queries using SELECT, WHERE, JOIN, GROUP BY, aggregate functions, and basic subqueries.

  • Work with a relational database such as PostgreSQL.

  • Use Git for basic version control, including commits, branches, and merging changes.

  • Install Python packages and work within a virtual environment.

  • Read Python tracebacks and SQL errors and perform basic debugging.

  • Write or understand basic automated tests.

  • Work comfortably from the command line and within a development environment such as VS Code.

  • Experience with APIs, Docker, cloud platforms, Parquet, data pipelines, or data engineering tools is helpful but not required. These topics are developed throughout the program.

Hardware/Software requirements

  • Personal laptop meeting our hardware & software requirements. See this blog post for full details of our laptop specs.

Tuition Details

There are two tuition plans: Standard Tuition or Nashville Tech Opportunity Tuition. A limited number of grants and scholarships are also available for students from underrepresented groups.

Standard Tuition

We request a deposit on acceptance of our offer of admission. The balance is due at enrollment on the first day of class. You may also elect to pay the balance during the program through an approved payment plan, as discussed below. See below for other financing and payment options.

Nashville Tech Opportunity Tuition

The NSS Nashville Tech Opportunity Tuition allows you to defer most of the cost of your training until you graduate and go to work. You will pay us a tuition deposit on acceptance to the program and then nothing more until after graduation.

This program is a mutual risk-sharing program between NSS and the Student. NSS invests in the student through a scholarship and through deferring the balance of the student's tuition until the student a) graduates and b) becomes employed as a developer. Until those conditions are met, the student does not owe NSS the tuition balance.

Once you go to work using the skills you learned at NSS, we'll work out a payment plan for you to reimburse NSS. We also have a limited number of partner companies that are willing to reimburse part or all of your tuition.

Selection criteria for the Nashville Tech Opportunity Tuition includes:
  • Limited to Nashville area residents or individuals who grew up in Nashville/Middle Tennessee and have strong family and personal connections to the area
  • Limited to students that are committed to staying in Middle Tennessee post-graduation
  • Priority is given to students whose economic circumstances would otherwise prevent them attending Nashville Software School
  • Priority is also given to individuals from groups that are underrepresented in tech careers (e.g. women, Veterans, Black individuals, etc.)

Scholarships

We have available a limited number of scholarships between $2,000 and $5,000 for high potential students with economically disadvantaged backgrounds or from groups that are underrepresented in technology careers.


Tuition Plan Details

Total Tuition Standard Tuition Deposit Standard Tuition Due By Day 1 of Class Opportunity Tuition Deposit Opportunity Tuition Scholarship Opportunity Tuition Deferred Tuition
Data Engineering With Python Foundation $14,000 $5,000 $9,000
(remaining balance)		 $1,500 $3,500 $9,000
(remaining balance)
Data Engineering Bootcamp $10,500 $4,000 $6,500
(remaining balance)		 $1,500 $2,500 $6,500
(remaining balance)

Financing/Payment Plans

Payment Plans | Data Engineering With Python Foundations Bootcamp

If you are paying for regular tuition out-of-pocket, there are three payment options:

Payment 1 Payment 2 Payment 3+ Total Cost
Option 1: Early-bird Discount $5,000 deposit due when offer is accepted. $8,000 due before the first day of class (reflects $1,000 early-bird discount). N/A Total cost: $13,000
Scholarship and student loan recipients are not eligible for the early-bird discount.
Option 2: Deposit + 2 Additional Payments $4,000 deposit due when offer accepted. $3,000 due before the first day of class. $7,000 due halfway through the course. Total cost: $14,000
Option 3: Deposit + Monthly Payments $4,000 deposit due when offer accepted. $1,300 due before the first day of class. $900 due on the 1st of each month for 10 months starting with the second month of the program. Total cost: $14,300


Payment Plans | Data Engineering Bootcamp

If you are paying for regular tuition out-of-pocket, there are three payment options:

Payment 1 Payment 2 Payment 3+ Total Cost
Option 1: Early-bird Discount $4,000 deposit due when offer is accepted. $5,500 due before the first day of class (reflects $1,000 early-bird discount). N/A Total cost: $9,500
Scholarship and student loan recipients are not eligible for the early-bird discount.
Option 2: Deposit + 2 Additional Payments $4,000 deposit due when offer accepted. $2,000 due before the first day of class. $4,500 due halfway through the course. Total cost: $10,500
Option 3: Deposit + Monthly Payments $4,000 deposit due when offer accepted. $1,200 due before the first day of class. $800 due on the 1st of each month for 7 months starting with the second month of the program. Total cost: $10,800

Student Loans

We are working with our student loan partners to provide financing options for the Data Engineering Bootcamp. We will provide details as we have them.

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